This paper provides a comprehensive analysis of computational methods for predicting anti-inflammatory peptides (AIPs). Inflammation, a biological response to harmful stimuli, can lead to chronic inflammatory diseases if prolonged. Peptide therapeutics have gained significant attention due to their high specificity and low toxicity. AIPs, which are peptides that regulate and reduce inflammation, have shown promise in treating various inflammatory and autoimmune diseases. The study reviews existing computational models for predicting AIPs, highlighting their diversity in datasets, feature representation, selection, training, and evaluation methods. The authors evaluate these models based on independent tests and AUC values, identifying challenges and prospects for developing more effective prediction models. The introduction emphasizes the importance of AIPs in controlling immune responses and maintaining tissue balance, making them valuable for novel therapeutic applications.This paper provides a comprehensive analysis of computational methods for predicting anti-inflammatory peptides (AIPs). Inflammation, a biological response to harmful stimuli, can lead to chronic inflammatory diseases if prolonged. Peptide therapeutics have gained significant attention due to their high specificity and low toxicity. AIPs, which are peptides that regulate and reduce inflammation, have shown promise in treating various inflammatory and autoimmune diseases. The study reviews existing computational models for predicting AIPs, highlighting their diversity in datasets, feature representation, selection, training, and evaluation methods. The authors evaluate these models based on independent tests and AUC values, identifying challenges and prospects for developing more effective prediction models. The introduction emphasizes the importance of AIPs in controlling immune responses and maintaining tissue balance, making them valuable for novel therapeutic applications.